Article Per Year (5 Year)
p-Index From 2021 - 2026
1.259
P-Index
This Author published in this journals
All Journal Proceedings of KNASTIK JOIV : International Journal on Informatics Visualization QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknoinfo Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Prosiding Konferensi Nasional PKM-CSR International Journal of Electronics and Communications Systems Joutica : Journal of Informatic Unisla Jurnal Teknik : Majalah Ilmiah Fakultas Teknik Unpak J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Andi Chairunnas
Universitas Pakuan
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Nursing Public Health Social Sciences Transportation

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
UPDATING DATABASE JEMBATAN LOKAL, KOLEKTOR DAN EVALUASI KONDISINYA JAKARTA SELATAN Mudianto, Arief; Chairunnas, Andi; Purwanti, Heny
Jurnal Teknik | Majalah Ilmiah Fakultas Teknik UNPAK Vol 11, No 2 (2010): Jurnal Teknik : Majalah Ilmiah Fakultas Teknik UNPAK
Publisher : Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/teknik.v11i2.1041

Abstract

Suku Dinas Pekerjaan umum jalan kota administrasi Jakarta Selatan sebagai salah satu unit pelaksana teknis pemerintah daerah ibu kota Jakarta bermaksud merumuskan development sistem database jembatan di Wilayah Jakarta Selatan, menggunakan perangkat lunak dengan tahapan implementasi untuk database Microsoft Access 2007 dan Visual Basic 6.0,  dengan Map Object dan Arcview serta Kristal Report yang digunakan untuk interface (tampilan antar muka), kode program (coding) dijalankan dengan program Visual Basic Beberapa kebutuhan data jembatan yang perlu dimasukkan ke dalam database jembatan diantaranya : dimensi, teknis struktur, lokasi, kondisi eksisting, pemanfaatan. Berdasar hasil pengamatan dan survey di lapangan  maka jumlah jembatan yang ada di Jakarta Selatan mencapai 214 jembatan. Dengan membuat sistem yang terkomputerisasi maka data/informasi mengenai Jembatan dapat diberikan setiap saat dengan sajian yang sesuai dengan kebutuhan.
Deteksi Insomnia Menggunakan Sensor GSR dan Max30102 Metode Naïve Bayes M. Taufiq Fadli R; Andi Chairunnas; M Iqbal Suriansyah
Joutica Vol 10 No 1 (2025): MARET
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/informatika.v10i1.1370

Abstract

Insomnia adalah gangguan tidur yang menurunkan kualitas istirahat dan membuat sulit tertidur atau mempertahankan tidur. Insomnia dapat menyerang berbagai kalangan dan disebabkan oleh berbagai faktor. Deteksi dini penting untuk mencegah insomnia menjadi kondisi serius. Polysomnography adalah metode medis konvensional untuk mendeteksi insomnia, namun memerlukan peralatan kompleks dan pasien harus menginap di rumah sakit. Untuk itu, penelitian ini mengusulkan deteksi dini insomnia dengan alat portabel berbasis sinyal elektrokardiogram (EKG) yang memiliki fitur P, Q, R, S, dan T yang dapat dianalisis. Metode yang digunakan adalah Naive Bayes, yang mengklasifikasikan data sebagai insomnia atau normal berdasarkan probabilitas tertinggi. Naive Bayes dipilih karena penelitian sebelumnya menunjukkan akurasi 80% dalam mendeteksi apnea tidur. Penelitian ini menggunakan mikrokontroler ESP32 dan sensor MAX30102 untuk akuisisi sinyal EKG, yang efisien dari segi biaya dan daya. Hasil penelitian menunjukkan akurasi sensor MAX30102 sebesar 97,73% dan sensor GSR sebesar 90% dalam mendeteksi aktivitas listrik pada kulit jari. Klasifikasi Naive Bayes mencapai akurasi 90% dalam membedakan antara kondisi normal dan insomnia.
Optimization of Stock Price Prediction Using Long Short-Term Memory (LSTM) Algorithm and Cross-Industry Standard Process Approach for Data Mining (CRISP-DM) Saepulrohman, Asep; Chairunnas, Andi; Denih, Asep; Safitri Yasibang, Nurdiana Dini
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.26727

Abstract

Predicting stock prices accurately is an integral part of investment analysis as it permits forecasting movements in the financial markets and tailoring strategies accordingly. In this study, the LSTM (Long Short-Term Memory) algorithm is used with the aim of improving predictive accuracy, particularly the forecasting of stock price movements. This research follows the CRISP-DM framework or Cross-Industry Standard Process for Data Mining, which incorporates six defined steps including: understanding the business context, data understanding, data preparation, model building, evaluation, and implementation. Stock price data for the ticker symbol “ANTM.JK” was sourced from Yahoo Finance for the date range of October 29, 2005 to July 11, 2024. Along with the consistency, several model accuracy enhancing preprocessing steps such as data cleaning, feature selection, and normalization with Python were performed before modeling. Hyperparameter tuning to reduce the error margins on predictions was conducted after training the LSTM model. Testing the hypotheses showed that the LSTM model demonstrated a low Root Mean Square Error (RMSE) on the test dataset indicating outstanding forecasting accuracy. The ability of the model to outperform conventional time series forecasting techniques is attributed to its ability to effectively retain nonlinear time-series relationships and long-term dependencies. These findings suggest that the LSTM algorithm can serve as a reliable tool for stock price forecasting in emerging markets. This study provides practical insights for investors and lays the groundwork for future research on hybrid or ensemble models to further improve prediction robustness and accuracy
Classification and Visualization Model of Stunting Zone Distribution Using Artificial Intelligence and Streamlit Approaches Zuraiyah, Tjut Awaliyah; Widanti, Nurdina; Yamato, Yamato; Chairunnas, Andi; Mauludin, Kriti; Setha, Bira Arya
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3174

Abstract

Time series datasets enable automated classification processes. Machine Learning (ML) and Deep Learning (DL) models are Artificial Intelligence (AI) models that allow systems to make intelligent decisions automatically. Stunting is a significant public health issue that warrants serious attention. Decision-making requires accurate, data-driven information that is easily understandable. However, many classification results have not been visualized in a way that allows users to understand them easily. This study aims to evaluate the performance of the classification model and visualize the distribution of areas using the Streamlit framework. The ML classification models used are Decision Tree and Extreme Gradient Boosting (XGBoost), while the DL classification models used are LSTM and Bi-LSTM models. The visualization tool was developed using the Python programming language integrated with the web-based Streamlit framework. SMOTE is used to balance the dataset, thereby improving accuracy. Stunting data were obtained from the Bogor City Health Office in the form of By Name By Address (BNBA) stunting data for 2022 - 2024, totaling 6023 data. The model performance is evaluated by assessing accuracy, precision, recall, and F1 score. The results show that the BiLSTM model performs better after data matching with SMOTE, achieving an accuracy of 99.43%. Bi-LSTM has two directions: forward (from past to future) and backward (from future to past). This intelligent system uses the BiLSTM model and is dynamic, providing an automatic display of stunting classification and distribution zones. So, stakeholders can use it to get recommendations for stunting decision-making and further research.
Robust Anomaly Detection in Network Traffic Using Bagging with Majority Voting Ensemble Sultan Ilham Seftiansyah, Muhammad; Chairunnas, Andi; Yanti, Yusma
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 1 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/j-koma.v8i1.03

Abstract

Anomaly detection in computer networks is a crucial aspect of ensuring system security and availability. One of the most common and disruptive threats is Distributed Denial of Service (DDoS) attacks, which can overload servers and compromise service continuity. Traditional Intrusion Detection Systems (IDS) often struggle to detect sophisticated and evolving attack patterns, leading to reduced detection performance. This research proposes the use of ensemble learning with Bagging and Majority Voting to enhance anomaly detection. The dataset used in this study was CIC-DDoS2019, consisting of 33,066 rows and 88 features, processed through data cleaning, label encoding, and normalization. Three base classifiers—Decision Tree, Random Forest, and XGBoost—were integrated using Bagging with Majority Voting. Experiments were conducted with different train-test split ratios of 70:30, 75:25, 80:20, and 90:10. The results showed that the 70:30 split achieved the best performance with an accuracy of 93.58%, an F1-score of 90.51%, and the fastest evaluation time of 142.86 seconds. Additional tests on spam and phishing datasets confirmed the robustness of the Bagging approach, achieving accuracy above 96%. These findings demonstrate that Bagging with Majority Voting can effectively improve IDS performance and provide a reliable solution for detecting various types of cyberattacks.
Co-Authors Agung Prahujana Putra Agung Prajuhana Putra Arie Qur’ania Arief Mudianto Asep Denih Asep Saepulrohman Daud Yusuf Dendy Saeful Zen Hadian Adam Herman Henny Annisa Rahmawati Heny Purwanti Iyan Mulyana Izey Nurdiansyah M Iqbal Suriansyah M. Taufiq Fadli R Mauludin, Kriti Mira Mirnawati Mudianto, Arief Nurdina Widanti Purwanti, Heny Safitri Yasibang, Nurdiana Dini Setha, Bira Arya Soewarto Hardhienata Sultan Ilham Seftiansyah, Muhammad Teguh Pribadi Tjut Awaliyah Zuraiyah Triyoga Ginanjar Pamungka Yamato, Yamato Yanti, Yusma